TRI-IVIS focuses on Trustable, Reproducible and Intelligent Information Visualization Systems, aiming to advance methods and systems for managing and analyzing large-scale complex data, integrating Artificial Intelligence, Machine Learning, Big Data analytics and advanced visual interfaces. While there are many domains that may benefit from such characteristics, preferred domains for the workshop are, but not limited to, genomic applications and Meetings, Incentives, Conferences and Events (MICE). Many topics revolve around such domains, ranging from heterogeneous large-scale data, distributed storage, multi-stakeholder scenarios, and regulatory requirements.

In recent years, growing attention to the gut microbiota has led to increasing use of complex clinical reports, such as those provided by microbiome and multi-omic analyses. Irritable bowel syndrome (IBS) is a relevant area of application, characterized by a chronic functional disorder of the gastrointestinal tract. Having clear and easily interpretable diagnostic tools increases the capacity of diagnosis and helps patients to better understand the content of the reports, which are often static and rich in scientific data, making them complex to understand for both patients and non-specialist physicians. This work proposes the development of a dynamic platform for the visualization and interactive management of microbiome clinical reports. Starting from existing reports, a prototype web interface capable of presenting data in a modular, adaptable, and customizable way has been designed and proposed. The prototype also allows the generation of reports in PDF format, both in a dynamic version (with the possibility of filtering and exploring different sections) and in a static version (useful for printing or clinical sharing). The added value of this solution lies in readability and quick access to the information.

Microbiome sequencing offers significant potential for advancing clinical diagnostics, but its adoption is hindered by challenges in data processing, standardization, and the translation of complex genomic data into actionable clinical insights. The GenDAI project addresses these challenges by developing a novel, integrated medical diagnostics platform that leverages Artificial Intelligence (AI), powered by Genomic Foundation Models (GFMs), to accelerate and improve the analysis of microbiome data. The platform's primary goal is to provide a fully automated, reproducible, and compliant end-to-end solution, from data ingestion to clinical reporting, to support personalized medicine, with an initial focus on Inflammatory Bowel Disease (IBD). This paper presents an overview of GenDAI's vision, outlining its user-centered methodological approach and the conceptual architecture of its core components. The architecture integrates four key pillars: (1) a fully automated and auditable diagnostics workflow, (2) a secure and compliant cloud platform for long-term data management based on Open Archival Information System (OAIS) and Findable, Accessible, Interoperable, Reusable (FAIR) principles, (3) an advanced AI engine for biomarker discovery using GFMs, and (4) interactive, usercentered reporting tools designed to enhance explainability and clinical trust. By providing a holistic and ethically-grounded framework, GenDAI aims to bridge the gap between advanced genomic research and practical clinical application.

This paper focuses on the aspects of responsibility and ethics when using AI systems in the contexts of health and genomics. The present proposal also aims to address the important need to consider how the use of AI systems in the field of genomics affects human life and its implications. As AI systems increasingly participate in diagnostic and predictive decision-making, they introduce new challenges concerning moral agency, transparency, and human oversight. The discussion aims to examine how these technologies, while offering unprecedented analytical capabilities, simultaneously reshape the ethical landscape of medical practice and genomic research.

Artificial intelligence has become integral to the life sciences, enabling large-scale data analysis and accelerating discovery. However, deploying these analytical capabilities depends on cloud infrastructures that are often complex to design, validate, and operate. Current approaches rely heavily on manual, time-consuming configuration by experts. This paper explores how artificial intelligence, specifically Large Language Models, can support not only genomic data analysis but also the automated creation of the underlying computational infrastructure. We propose a methodology that translates naturallanguage user stories and requirements into cloud-architectural specifications and containerized deployments, bridging the gap between user intent and executable infrastructure. Preliminary results indicate that LLM-driven synthesis can reduce human effort in defining container relationships and deployment logic, advancing automation in cloud-native bioinformatics.

This paper presents a cloud-native architecture for clinical metagenomic diagnostics developed as part of the Horizon Europe project GenDAI. The architecture integrates automated, reproducible, and auditable workflows with deterministic elasticity, compliant data stewardship, explainable Artificial Intelligence (AI), and verifiable reporting. Requirements derived from clinical practice inform a unified modeling, implementation, and evaluation strategy for a modular platform that combines workflow orchestration, data governance, AI-powered modeling, and cloud-native reporting. The system embeds provenance-bydesign, policy-as-code enforcement, and deterministic elasticity across all layers to enable reproducible, compliant, and trustworthy metagenomic diagnostics. This work provides a principled pathway for translating research-grade tools into regulator-ready diagnostic services while maintaining transparency, reproducibility, and long-term trust.